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Abstract Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Abstract Objective New‐onset seizure‐like events (SLEs) are common in children, but differentiating between epilepsy and its mimics is challenging. This study provides an overview of the clinical characteristics, diagnoses, and corresponding etiologies of children evaluated at a first seizure clinic (FSC), which will be helpful for all physicians involved in the care of children with SLEs. Methods We included 1213 children who were referred to the FSC of a Dutch tertiary children's hospital over a 13‐year period and described their clinical characteristics, first routine EEG recording results, and the distribution and specification of their eventual epilepsy and non‐epilepsy diagnoses. The time interval to correct diagnosis and the diagnostic accuracy of the FSC were evaluated. Results “Epilepsy” was eventually diagnosed in 407 children (33.5%), “no epilepsy” in 737 (60.8%), and the diagnosis remained “unclear” in 69 (5.7%). Epileptiform abnormalities were seen in 60.9% of the EEG recordings in the “epilepsy” group, and in 5.7% and 11.6% of the “no epilepsy” and “unclear” group, respectively. Of all children with final “epilepsy” and “no epilepsy” diagnoses, 68.6% already received their diagnosis at FSC consultation, and 2.9% of the children were initially misdiagnosed. The mean time to final diagnosis was 2.0 months, and 91.3% of all children received their final diagnosis within 12 months after the FSC consultation. Significance We describe the largest pediatric FSC cohort to date, which can serve as a clinical frame of reference. The experience and expertise built at FSCs will improve and accelerate diagnosis in children with SLEs. Plain language summary Many children experience events that resemble but not necessarily are seizures. Distinguishing between seizures and seizure mimics is important but challenging. Specialized first‐seizure clinics can help with this. Here, we report data from 1213 children who were referred to the first seizure clinic of a Dutch children's hospital. One‐third of them were diagnosed with epilepsy. In 68.8% of all children—with and without epilepsy—the diagnosis was made during the first consultation. Less than 3% were misdiagnosed. This study may help physicians in what to expect regarding the diagnoses in children who present with events that resemble seizures.

PurposeWe investigated the distribution of spikes and HFOs recorded during intraoperative electrocorticography (ioECoG) and tried to elaborate a predictive model for postsurgical outcomes of patients with lateral neocortical temporal lobe epilepsy (TLE) whose mesiotemporal structures are left in situ.MethodsWe selected patients with temporal lateral neocortical epilepsy focus who underwent ioECoG-tailored resections without amygdalo–hippocampectomies. We visually marked spikes, ripples (80–250 Hz), and fast ripples (FRs; 250–500 Hz) on neocortical and mesiotemporal channels before and after resections. We looked for differences in event rates and resection ratios between good (Engel 1A) and poor outcome groups and performed logistic regression analysis to identify outcome predictors.ResultsFourteen out of 24 included patients had a good outcome. The poor-outcome patients showed higher rates of ripples on neocortical channels distant from the resection in pre- and post-ioECoG than people with good outcomes (ppre = 0.04, ppost = 0.05). Post-ioECoG FRs were found only in poor-outcome patients (N = 3). A prediction model based on regression analysis showed low rates of mesiotemporal post-ioECoG ripples (ORmesio = 0.13, pmesio = 0.04) and older age at epilepsy onset (OR = 1.76, p = 0.04) to be predictors of good seizure outcome.ConclusionHFOs in ioECoG may help to inform the neurosurgeon of the hippocampus-sparing resection success chance in patients with lateral neocortical TLE.

Temporal lobe epilepsy (TLE) is the most common form of refractory focal epilepsy and is often associated with hippocampal sclerosis (HS) and cognitive disturbances. Over the last decade, high frequency oscillations (HFOs) in the intraoperative electrocorticography (ioECoG) have been proposed to be biomarkers for the delineation of epileptic tissue but hippocampal ripples have also been associated with memory consolidation. Healthy hippocampi can show prolonged ripple activity in stereo- EEG. We aimed to identify how the HFO rates [ripples (80–250 Hz, fast ripples (250–500 Hz); prolonged ripples (80–250 Hz, 200–500 ms)] in the pre-resection ioECoG over subtemporal area (hippocampus) and lateral temporal neocortex relate to presence of hippocampal sclerosis, the hippocampal volume quantified on MRI and the severity of cognitive impairment in TLE patients. Volumetric measurement of hippocampal subregions was performed in 47 patients with TLE, who underwent ioECoG. Ripples, prolonged ripples, and fast ripples were visually marked and rates of HFOs were calculated. The intellectual quotient (IQ) before resection was determined. There was a trend toward higher rates of ripples and fast ripples in subtemporal electrodes vs. the lateral neocortex (ripples: 2.1 vs. 1.3/min; fast ripples: 0.9 vs. 0.2/min). Patients with HS showed higher rates of subtemporal fast ripples than other patients (Z = −2.51, p = 0.012). Prolonged ripples were only found in the lateral temporal neocortex. The normalized ratio (smallest/largest) of hippocampal volume was correlated to pre-resection IQ (r = 0.45, p = 0.015). There was no correlation between HFO rates and hippocampal volumes or HFO rates and IQ. To conclude, intra-operative fast ripples were a marker for HS, but ripples and fast ripples were not linearly correlated with either the amount of hippocampal atrophy, nor for pre-surgical IQ.

High frequency oscillations (HFOs, 80–500Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting. Keywords: Magnetoencephalography, Epilepsy, Beamformer, Virtual sensors, Automatic detection, High frequency oscillations

Epilepsy surgery requires precise localization of the epileptic source. EEG-correlated functional MRI (EEG-fMRI) is a new technique showing the haemodynamic effects of interictal epileptiform activity. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult surgical candidates considered ineligible because of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Interictal epileptic discharges (IEDs) in the EEG during fMRI were identified by consensus between two observers. Topographically distinct IED sets were analysed separately. Only patients with significant, positive blood oxygen level-dependent (BOLD) responses that were topographically related to the EEG were re-evaluated for surgery. Forty-six IED sets from 29 patients were analysed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus (n = 3), presumed multifocality (n = 2) or a combination of both (n = 3). EEG-fMRI improved localization in four out of six unclear foci. In patients with presumed multifocality, EEG-fMRI advocated one of the foci in one patient and confirmed multifocality in four out of five patients. In four patients EEG-fMRI opened new prospects for surgery and in two of these patients intracranial EEG supported the EEG-fMRI results. In these complex cases, EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. It is thus a valuable tool in the presurgical evaluation of patients. Guidelines for the use of EEG-fMRI in clinical practice are proposed. [ABSTRACT FROM AUTHOR]